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Full-Physics Inverse Learning Machine for satellite remote sensing of ozone profile shapes and tropospheric columns

Xu, Jian und Heue, Klaus-Peter und Coldewey-Egbers, Melanie und Romahn, Fabian und Doicu, Adrian und Loyola, Diego (2018) Full-Physics Inverse Learning Machine for satellite remote sensing of ozone profile shapes and tropospheric columns. ISPRS. ISPRS Technical Commission III Symposium 2018, 2018-05-07 - 2018-05-10, Beijing, China. doi: 10.5194/isprs-archives-XLII-3-1995-2018.

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Offizielle URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/

Kurzfassung

Ozone plays a crucial role in the Earth’s atmosphere and its chemical processes (production and destruction) are related to climate change and air pollution caused by anthropogenic emissions. Therefore, accurate information about global/regional vertical distributions of ozone in the troposphere and stratosphere turns out to be important to scientific communities. Spaceborne remote sensing of ozone information using the ultraviolet (UV) radiation has been comparatively mature, total columns have been successfully estimate by the Differential Optical Absorption Spectroscopy (DOAS) algorithm. However, characterizing ozone profile shapes from nadir-viewing satellite measurements is still known to be challenging, particularly the ozone information in the troposphere. As most atmospheric ozone resides in the stratosphere above, tropospheric ozone columns can be derived by subtracting an estimate of the stratospheric columns or by differencing total columns in cloud-free pixels from those in nearby pixels with thick/high convective clouds (the so-called CCD method). Retrieval of tropospheric ozone abundances can largely benefit in terms of representativity by obtaining reliable an ozone profile shape. Direct retrieval of tropospheric information has also been exploited and applied to the Global Ozone Monitoring Experiment (GOME) class of instruments. In general, the direct estimation of atmospheric parameters of interest from spectral measurements is treated as an ill-posed inverse problem that often requires an iterative inversion of large matrices and multiple calls to radiative transfer calculations. However, this classical inversion method is computationally expensive and reliable a priori knowledge can be vital to the retrieval outcome. Alternatively, the above-mentioned inverse problems can be solved by means of machine learning. Therefore, we propose a novel retrieval algorithm called Full-Physics Inverse Learning Machine (FP-ILM) and estimate ozone profile shapes from GOME-2 measurements on the MetOp series of satellites. Unlike traditional inversion methods, the FP-ILM algorithm formulates the retrieval of ozone profile shapes as a classification problem. The implementation of FP-ILM comprises a training phase to derive an inverse function from synthetic measurements using a radiative transfer model in conjunction with the "smart sampling" approach, and an operational phase in which the inverse function is applied to real measurements. In particular, the employed forward model symbolizes the “full-physics” feature. With the aid of machine learning techniques, FP-ILM has been proven to produce a noticeable increase in computational speed and a reasonable retrieval accuracy when dealing with satellite measurements. The comparison of retrieved ozone profiles between FP-ILM and the optimal estimation method reaches a promising agreement. This paper extends the ability of the FP-ILM retrieval to derive tropospheric ozone columns from GOME-2 measurements. Results of total and tropical tropospheric ozone columns are compared with the ones using the DOAS and CCD methods, respectively, and further comparisons of ozone profiles are conducted with ozonesonde observations. Furthermore, the FP-ILM framework will be used for the near-real-time processing of the new European Sentinel sensors with their unprecedented spectral and spatial resolution and corresponding large increases in the amount of data.

elib-URL des Eintrags:https://elib.dlr.de/119388/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Full-Physics Inverse Learning Machine for satellite remote sensing of ozone profile shapes and tropospheric columns
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Xu, Jianjian.xu (at) dlr.dehttps://orcid.org/0000-0003-2348-125XNICHT SPEZIFIZIERT
Heue, Klaus-PeterKlaus-Peter.Heue (at) dlr.dehttps://orcid.org/0000-0001-8823-7712NICHT SPEZIFIZIERT
Coldewey-Egbers, MelanieMelanie.Coldewey-Egbers (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Romahn, Fabianfabian.romahn (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Doicu, AdrianAdrian.Doicu (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Loyola, DiegoDiego.Loyola (at) dlr.dehttps://orcid.org/0000-0002-8547-9350NICHT SPEZIFIZIERT
Datum:2018
Referierte Publikation:Nein
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Band:XLII-3
DOI:10.5194/isprs-archives-XLII-3-1995-2018
Seitenbereich:Seiten 1995-1998
Verlag:ISPRS
Name der Reihe:ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”
Status:veröffentlicht
Stichwörter:Atmospheric composition measurements, satellite remote sensing, machine learning, ozone, UV spectroscopy
Veranstaltungstitel:ISPRS Technical Commission III Symposium 2018
Veranstaltungsort:Beijing, China
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:7 Mai 2018
Veranstaltungsende:10 Mai 2018
Veranstalter :ISPRS Technical Commission III on Remote Sensing
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Atmosphären- und Klimaforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Atmosphärenprozessoren
Hinterlegt von: Xu, Dr.-Ing. Jian
Hinterlegt am:20 Mär 2018 11:15
Letzte Änderung:24 Apr 2024 20:23

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